Strengthening the Dairy Value Chain Progress_May 2012

Analysis of SDVC Data
March 2009 - April 2012

Presented May 31, 2012

VACCINATION
Vaccination of cattle is most
beneficial for more wealthy
households. In poorer households,
the use of vaccination does
not seem to increase income.
However, in wealthier households,
income can increase by about 3%
if the cattle are vaccinated.
wealthy
However, for all households – the household income is related to
vaccination provider choices.
Households that have lower then average incomes use CARE

3%
Livestock Health Workers or Other Livestock Health Workers or
a Government Vet. Households that have higher than average
at least incomes tend to use their own family members to provide
vaccinations.

ARTIFICIAL INSEMINATION DEWORMING
Use of Artificial Insemination Deworming of cattle has a very positive effect
increases for all households. on household income for all households.
The average household can The average household
expect to see at least a 3% can expect an increase in
increase in household income income of between 5 and
from milk if they use artificial 10% if they deworm their
insemination. cattle.

5%-10%
income increase
Whether or not a household uses Artificial
The strongest predictor of whether or not a household chooses to deworm their cattle is their overall

3 % Insemination is strongly predicted by the availability
knowledge score and the level of confidence they feel in their Livestock Health Worker. A household
of the service, the economic status of the household
with a high knowledge score and a high level of confidence in their Livestock Health Worker is 30%
at least and the skills of the household’s Livestock Health more likely to deworm their cattle than a household with a low knowledge score and a low level of
Worker.
confidence in their Livestock Health Worker.

Interestingly, a household
ARTIFICIAL INSEMINATION with a low knowledge
score and a high level PERCENT OF DEWORMING
USAGE RATES OVER TIME of confidence in their
LHW and a household USAGE RATES OVER TIME
with a high knowledge
score but a low level
23.3% 8.6% 7.1% 7.4% 13.8% 11% 10.3% 8.8%
of confidence in their 79% 58% 51% 42% 41% 34% 31% 47%
LHW are both about
equally likely to deworm
their cattle. Both are
about 15% less likely
to deworm their cattle
mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 than the high knowledge mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
and high confidence
household.

GROUP LEADER BY GENDER
Overall, Households within Learning
Groups with Female Leaders have
incomes that are 3-6% higher.

3% 6%
-
higher income

GROUP LEADER BY PHASE GROUP LEADER BY GROUP COMPOSITION
Learning Groups with Female Leaders do relatively better as the Phase progresses Learning Groups with a high percentage
GROUP COMPOSITION of women producers with a female group

FARM LEADER GENDER LEADER GENDER leader perform the best overall.

Learning Groups with a high percentage
group farm of men producers do moderately well

IMPROVES INCOME FROM MILK
composition leader gender
regardless of group leader gender.

Learning Groups with a high percentage of
women producers and a male group leader
perform the least well.
percent
increase
12%

7%
5%
2% percent improved
0% performance over
male leaders
female leader phase 1 phase 2 phase 3 phase 4 2%

In Phase 1, the groups with female leaders do 7% better
In Phase 2, the groups with female leaders do 5% better
In Phase 3, the groups with female leaders do 2% better than groups with male leaders
In Phase 4, there is no difference in income between female led groups and male led groups. or
5%

MARKET LINKAGE How and where a household sells their milk
significantly affects their income.

Market Linkage by household economic status
poor wealthy poor wealthy poor wealthy

GRAMEEN
INFORMAL MV RD PRAN BRAC
DANOON AKIJ
MARKET MARKETS MARKETS

5-8%
The poorest households However, the rich households do much There is a slight advantage
GROUP ECONOMIC STATUS
AND

do the same as the better than the poor households when to the wealthier households
wealthier households when they sell their milk to the MV, RD, is the Grameen Danoon and
LARGER ECONOMIC CONTEXT selling their milk in an PRAN, and BRAC markets. When all Akij markets, but it is much
The initial economic status and the larger economic informal market. else is equal, a rich household makes less consistent.
environment of a group has a heavy influence on their between 5-8% more money than a
milk income. poor household when selling milk in

In general, if a group is poor initially, their progress is
Market Linkage by household these markets.
WHAT PERCENT DO HOUSEHOLDS FROM
better if they operate within a wealthy District. economic status and presence of a group selected RICH GROUPS DO BETTER
thier distri orer distric
milk collector At MV & RD, the households making THAN HOUSEHOLDS FROM POOR GROUPS
weal ct po t the most money are wealthy and do wealthy

On the informal markets, the not have their own collectors.
6.19% 5.48% 4.78% 4.21%
poorer households with their MV RD PRAN BRAC

own collectors do the best. At PRAN and BRAC, the households
doing the best are wealthy with their 3.49 %
2.05 %
1.47 %
0.88%
AKIJ GRAMEEN INFORMAL OTHER
own milk collector. DANNON SECTOR
poor

poor

poor wealthy wealthy wealthy

At Grameen Dannon, most
households do the same
– with the very significant
5% 7%
- A poor Learning Groups that
operate within one of the
exception of the very
poor households without
better in wealthier Districts do 5-7% their own collector. These
earning income better in earning income than At Akij, it seems
households do very poorly
to be irrelevant if
equivalent poor Learning at this market.
you have your own
Groups that operate within collector or not.
one of the poorer Districts.

And in general, a group that is more
INFORMAL MV RD PRAN BRAC
wealthy to begin with a operates MARKET
within a wealthy District does the MARKETS
best overall – a full 10% - 12% better 10%-12% GRAMEEN
than even an equivalently rich group
better in DANOON AKIJ
that operated in a poor District.
earning money MARKETS

CATTLE SELLING DECISIONS
Households in which women own cattle and women make
the cattle selling decisions are more likely to sell cattle and are
more likely to have higher incomes overall.

PERMISSIONS TO ATTEND MEETINGS
Whether or not women producers need permission to attend meetings, both
within and outside of their village is influenced by whether or not they own
cattle, the economic status of their group and time.

Women who own cattle need less permission to attend meetings.

PERMISSION TO ATTEND MEETINGS
HOUSEHOLDS IN WHICH WOMEN OWN CATTLE -1.45 -1.25 -1.1 -0.97 -0.8 -0.6 -0.5 -0.45
low income
learning group
Households where women own cattle -1.6 -1.35 -1 -0.75 -0.48 -0.25 0.05 0.4
do about 10% better in earning
money than do households where -0.8 -0.6 -0.47 -0.3 -0.07
-1.2 -1 -0.9
women do not own cattle. high income

10%
learning group
-1.43 -1.05 -0.82 -0.5 -0.3 -0.05 0.4 0.55
mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12
However, this relationship is complex
and is changing over time. better in
earning money

Women who own cattle are less likely However, the rates of women needing
GENDER: GROUP AND HOUSEHOLD to need permission to attend meetings
far away.
permission to attend meetings is dropping
amongst women who don’t own cattle.

group has few -0.22 -0.13 -0.04 0.04 0.13 0.22 0.31 0.4
households where
women own cattle
-0.21 -0.11 -0.028 0.06 0.14 0.23 0.33 0.41
PERMISSION TO ATTEND FAR AWAY MEETINGS
group has many -0.45 -0.36 -0.27 -0.18 -0.09 -0.004 0.08 0.17 0.88 0.98 1.08 1.17 1.27
0.58 0.68 0.78
households where low income
women own cattle learning group
-0.34 -0.25 -0.17 -0.08 0.01 0.1 0.19 0.28
2.56 2.48 2.38 2.27 2.17 2.07 1.96 1.86

0.37 0.47 0.57 0.67 0.77 0.86 0.96 1.06
high income
learning group
2.37 2.27 2.16 2.06 1.96 1.85 1.75 1.65

Women in high income learning
groups are slightly more likely to
need permission to attend meetings.

LIVESTOCK HEALTH WORKERS
Livestock Health Workers income is influenced by:
• the gender of the worker
• the training the worker received
• whether or not the worker received a loan.

TRAINING BY SEX IS IMPORTANT
Female LHW with basic Female LHW with Female LHW with both
training achieve a 33% advanced training achieve basic and advanced training
higher income increase a 22% higher income achieve a 17% higher
than men. increase than men. income increase than men.

SEX BY RECEIVE LOAN IS IMPORTANT
Female LHW with loans have a 35% Female LHW without loans have a 24%
higher increase in income than men. higher increase than men.
MILK COLLECTORS
Milk collectors income is most influenced by the sex of the collector in combination
with the market linkage of the collector
LIVESTOCK HEALTH WORKERS INCOME
BRAC
Women milk collectors BASIC 33%
MILK COLLECTORS INCOME who sell here can expect
a 100% higher income
BRAC 100% increase over time than
men collectors selling ADVANCE 22%
here.
AKIJ 80%
Akij
GRAMEEN
Women milk collectors
DANNON 30%
who sell here can expect BOTH 17%
a 80% higher income
INFORMAL -10% increase over time than FEMALE LHW LEVEL OF TRAINING IMPROVEMENT
men collectors selling over MALE LHW with
the same training
MV RD PRAN NA here.
WOMEN
MILK COLLECTOR MARKET INCREASE OVER MEN MC Grameen Dannon
Women milk collectors
who sell here can expect 35%
a 30% higher income
Informal
increase over time than
Women selling here had an income increase that was 10%
men collectors selling
lower than men (3%)
here.
MV
Very few women collectors sell milk here. The few that do 24%
achieve a much higher income increase than the male milk
collectors.
FEMALE LHW LOAN IMPROVEMENT
RD & PRAN over MALE LHW with
the same loan status
Do not have enough women selling milk here to discuss.

FEED SOURCE COMPARED
94.9% 0.19 0.3 0.02
RICE BRAN
BDT %

CARBOHYDRATES
58.6% 0.05 0.5 0.30
WHEAT BRAN
BDT %

4.4% 0.04 0 0.30
PULSE HUSK
BDT %

45.4% 0.06 0.3 0.60
BROKEN RICE
BDT %

5.5% 0.04 0.8 0.60
OIL CAKE

PROTEINS
BDT %

21.4% 0.03 0.8 0.30
For the best nutrition, cattle need a M. OIL CAKE
%
ates
BDT
combination of Carbohydrates, Proteins Pro
and Vitamins and Minerals. dr te

VITAMINS &
hy

OTHER MINERALS
26.1% 0.17 0.1 0.20
The most cost effective and beneficial VITAMINS
bo

in
& MINERALS BDT %
forms of carbohydrates seems to be Wheat

s
CATTLE
Car

Bran and Broken Rice.
3.6% 0.08 0.1 0.10
READY FEED
Over time, our farmers have increased %
NUTRITION
BDT
their Wheat Bran use from 50% to 75%
feed source % of households average cost increase % increase in
of all households. And our farmers have using this feed per kg in taka per litre month milk income

held their rates of Broken Rice steady over per monthly 10 kg increase

time. About half of all households use
ls

Vit
broken rice.
ra

a
m
ne
FEED SOURCE FEED SOURCE
isn
Mi
PROPORTIONS PRICE OVER TIME
75 % WHEAT
BRAN
The most cost effective and beneficial
forms of proteins are various forms of Oil
Cakes.
RICE BRAN
18%

4%
57%

10%
31%

9%
10%

3%
8%

2%
9%

2%
7%

3%
RICE BRAN
0.25

BDT
0.20

BDT
0.19

BDT
0.21

BDT
0.16

BDT
0.16

BDT
0.15

BDT
0.19

BDT
CARBOHYDRATES

0.25 0.20 0.19 0.21 0.16 0.16 0.15 0.19
WHEAT BRAN
READY FEED
BDT BDT BDT BDT BDT BDT BDT BDT

Over time, our farmers have increased PULSE HUSK
0% 0.7% 0.3% 0% 0.1% 0.2% 0.3%
0.06 0.06 0.06 0.05 0.04 0.04 0.04 0.05
Vitamins and minerals are very important their use of various types of oil cakes by
WHEAT BRAN

for the health and milk production of cattle.
4% 7% 4% 1% 0.7% 0.8% 0.9%

about 10% overall. BROKEN RICE
0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04
PULSE HUSK

0.8% 0.2% 62% 2% 72% 0.2% 0.1%
Over time, our farmers have increased OIL CAKE
0.09 0.07 0.05 0.04 0.04 0.05 0.05 0.06

10
PROTEINS

%
BROKEN RICE
their regular use of vitamins and minerals BDT BDT BDT BDT BDT BDT BDT BDT

OIL
0% 2% 1% 0.3% 0.3% 0.3% 0.4%

by about 20% overall. M. OIL CAKE 0.23 0.03 0.02 0.57 0.03 0.31 0.02 0.19

CAKES VITAMINS
VITAMINS &

& MINERALS
4% 0.6% 0.5% 0.9% 0.4% 0.1% 0.1%
OTHER MINERALS

VITAMINS 0.06 0.04 0.05 0.03 0.04 0.04 0.04 0.04
& MINERALS OIL CAKE

20%
VITAMINS 2.3% 3.6% 3.2% 1.1% 2.3% 1.2% 1.1%
0.00 0.04 0.04 0.04 0.04 0.04 0.04 0.03
READY FEED
MINERALS M. OIL CAKE

jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 overall
average

Overview of Entire Dataset:
Overview of Household Compostion - Entire Dataset Count of In-milk Local Breed Cows
Household Overview Count Percent
0 4192 46.09%
1 4138 45.50%
2 690 7.59%
3 75 0.82%
Respondents' Gender Total 9095 100.00%
Count Percent
1 Women 7290 80.15% Count of In-milk Cross Breed Cows
2 Men 1805 19.85%
9095 100.00% Count Percent
Total
0 8093 88.98%
1 800 8.80%
2 173 1.90%
3 29 0.32%
Total 9095 100.00%
Count of Households that have Cattle
Owned by Women
Count Percent Count of Total In-Milk Cows in Household
1 Yes 1202 13.22% Count Percent
2 No 6248 68.70% 0 3302 36.31%
Total 7450 100.00% 1 4734 52.05%
2 935 10.28%
3 124 1.36%
Total 9095 100.00%

Vet Practices

Type of Treatment Provider, in general
Count Percent

6093 66.99%
1 CARE LHW

Count of Households who Dewormed Cattle 1305 14.35%
2 Other LHW
Count Percent 344 3.78%
1 Yes 3589 39.46% 3 Govt Vet
2 No 5382 59.18% 4 Other people of 107 1.18%
Total 8971 100.00% DLS
5 Milk Processor 39 0.43%
Vet
6 Medicine/Feed 10 0.11%
Compant Vet

30 0.33%
7 Kabiraj
8 Own Family 7 0.08%
Count of Households Who Got AI for Cattle Member
Count Percent 63 0.69%
1 Yes 943 13.81% 9 Others
2 No 5884 86.19% 7998 100.00%
Total 6827 100.00% Total

Financial Practices

Count of Households that Got Loans
Count Percent
1 Yes 126 1.39%
2 No 8969 98.61%
Source of Loans for Households
Total 9095 100.00% that Got Them
Count Percent
1 Relatives 7 5.56%
2 MFI 87 69.05%
3 Commercial Bank 7 5.56%
4 Merchent 2 1.60%
Count of Households that Engaged
in Group Savings 5 Govt Institution 2 1.59%
6 Milk Processing Company 1.59%
2
Count Percent
7 Milk Trading Association 7.14%
9
1 Yes 1165 55.19%
8 Other Association 7 5.56%
2 No 946 44.81%
9 Others 3 2.38%
Total 2111 100.00%
Total 126 100.00%

Gender Roles Gender of Person Engaged in Feed Purchase
Count Percent
1 Women 653 7.18%
2 Men 6529 71.79%
3 Both 1051 11.56%

Count of Women Who Need Permission to Gender of Person Engaged with Milk Selling
Attend Group Meetings Count Percent
Count Percent 1 Women 2279 25.06%
1 Yes 3670 40.35% 2 Men 2765 30.40%
2 No 3898 42.86% 3 Both 871 9.58%
Total 7568 100.00% Total 5915 100.00%

Count of Women Who Need Permission to Gender of Person Engaged in Cow Rearing
Attend Meetings at a Distance
Count Percent
Count Percent 1 Women 5037 55.38%
1 Yes 6534 86.34% 2 Men 892 9.81%
2 No 1034 13.66% 3 Both 3166 34.81%
Total 7568 100.00% Total 9095 100.00%

Cattle Productivity

Cross Breed Cow Productivity (Daily Litres) Over Time and According to Phase
8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

0.00 Phase 1 Phase 2 Phase 3 Phase 4
Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12

Local Breed Cow Productivity (Daily Litres) Over Time and According to Phase
1.80

1.60

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.00

Phase 1 Phase 2 Phase 3 Phase 4

Knowledge & Practical Education

Total Knowledge Score Over Time and According to Phase
8

7

6

5

4

3

2

1

0


Total Practical Score Over Time and According to Phase
12

10

8

6

4

2

0

Feed Costs & Milk Income

Monthly Feed Costs per Cow (taka) Over Time and According to Phase
1200.00

1000.00

800.00

600.00

400.00

200.00

0.00


Monthly Income per Cow (taka) Over Time and According to Phase
1200.00

1000.00

800.00

600.00

400.00

200.00

0.00


Ratio of Milk Income to Feed Costs Over Time and According to Phase
2.50

2.00

1.50

1.00

0.50

0.00


Cattle Productivity

Percent of Women Who Need Permission to Attend Group Mee Over Time and According to Phase
70%
60%
50%
40%
30%
20%
10%
0%


Percent of Women Who Need Permission to Attend Group Mee Over Time and According to Phase

100%

80%

60%

40%

20%

0%


Where Milk is Sold
1= Percent milk consumed by household 2=Percent milk spoiled
3= Percent milk sold to neighbors 4=Percent milk sold on open markets
5= Percent milk sold to tea shops 6= Percent milk sold to milk collector
7= Percent milk sold to sales point 8= Other
Phase One Groups Over Time
March 2009 June 2009 October 2009 March 2010
20%

25%
24%

29%

35%
0%

43%
4%

47%

0%
0%

3%
3%
53%
11%

5%
0%

14%
13%

5%
7%
10%

0%

12%
15%
13%

1%

2%
1%
3%
2%

0%

1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8

August 2010 January 2011 August 2011 April 2012

25%
26%
27%

35%
33%
29%

39%

42%

0%
0%

4%
0%

3%
0%

3%
6%

14%
10%
9%

10%
10%

6%

5%
16%
7%
10%
15%
11%

1%
1%

2%
1%

1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8

Phase Four Groups Over Time
August 2011 April 2012

24%
27%
27%

31%

0%

6%
0%

6%

9%
12%
21%

2%

17%
15%
2%

1%

1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8

Data Collectiong & Variables

Summary of Statistical Models Used

SDVC has collected and analyzed over 350 variables encompassing 863 groups, 45 field facilitators and 2 regions spanning 4
years.

The data has been collected at the household level, the static group level, and the dynamic group level (which changes over
time) over eight waves from 2009-2012.

Given this, advanced statistical methods are required to produce accurate results.

Household
Level
Data
Sta=c
Group
Level
Data
Dynamic
Group
Level
Data

Household
ID
Count
of
Milk
Group
ID
Phase
Region
PPT
Round
PPT
Round
PPT
Round

all
cows
product.
1
2
3

737
1
.25
10111
1
1
35
47
75

1601
1
1.6
10111
1
1
35
47
75

2492
3
4.25
20245
2
1
NA
57
90

4962
2
2.5
30865
3
2
NA
NA
82

Data Analysis Methods
Generalized Linear Mixed-Effects models
Summary of Statistical Models Used

To accurately analyze the evidence on how SDVC interventions are working, we built statistical models that looked at all the levels simul-
taneously and controlled for the context in which the household exists (in this case, we included various group and program level vari-
ables).

Most of the trends and effects presented in the findings have controlled for many confounding and mediating variables in addition to the
primary variables of interest, including:
Geographic variables (upazila, region)
Group effect (group number, group contextual variables)
Household differences (family size, number of cows, breed of cows).

We used the R software for statistical computing. R is a free software environment that is widely used by statisticians. R is powerful and
uses the most up to date algorithms available due to its open source nature. The R packages contain functions for working with the com-
plex type of data that is involved in this project. These functions are not established in most other statistical packages.

We primarily used R to build mixed-effects regression models with both fixed and random effects. This we essential for accuracy as this
data has both nested effects (such as households within groups within regions) and crossed effects (such as groups within phases within
PPT rounds).

Due to the complex nature of the data, all of the models in this analysis were done using generalized linear mixed-effect models. Each of
the models in this presentation control for the size of the household cattle herd, the phase of the learning group of the household, the ef-
fects of time on the outcomes, and the contextual difference between the household’s results and the group’s results (ie. the within-house-
hold trend and the between-household trend.)

Generalized linear mixed-effect models (GLMMs) are a class of models designed for the analysis of clustered and longitudinal data with
non-normal dependent variables. In our models we have used a binomial link funtion and a penalized quasi-likelihood methods. All our
models include both a random intercept and a random slope. Each model includes fixed effects such as size of herd, time of collection
and phase of group. Each model also includes a series of random effects including the learning group and time. This method properly
controls for the fact that each group is meaured repeatedly as well as the fact that the data is clustered in several dimensions (ie. phase and
geography)

The acceptable significance level for all of our models is alpha = 0.05.

Strengthening the Dairy Value Chain Progress_May 2012

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